Edge Computing Resource Allocation for Dynamic Networks

动态网络的边缘计算资源分配

• 批准号: EP/T021942/1
• 负责人: Nikolaos Athanasopoulos
• 金额: $ 32.4万
• 依托单位: Queen's University Belfast
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT021942%2F1
• 关键词: Edge; Computing; Resource; Allocation; Dynamic

The potential offered by the abundance of sensors, actuators and communications in IoT era is hindered by the limited computational capacity of local nodes, making the distribution of computing in time and space a necessity. Several key challenges need to be addressed in order to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. Our research takes upon these challenges by dynamically distributing resources when the demand is rapidly time varying. We first propose an analytic mathematical dynamical modelling of the resources, offered workload, and networking environment, that incorporates phenomena met in wireless communications, mobile edge computing data centres, and network topologies. We also propose a new set of estimators for the workload and resources time-varying profiles that continuously update the model parameters. Building on this framework, we aim to develop novel resource allocation mechanisms that take explicitly into account service differentiation and context-awareness, and most importantly, provide formal guarantees for well-defined QoS/QoE metrics. Our research goes well beyond the state of the art also in the design of control algorithms for cyber-physical systems (CPS), by incorporating resource allocation mechanisms to the decision strategy itself. We propose a new generation of controllers, driven by a co-design philosophy both in the network and computing resources utilization. This paradigm has the potential to cause a quantum leap in crucial fields in engineering, e.g., Industry 4.0, collaborative robotics, logistics, multi-agent systems etc. To achieve these breakthroughs, we utilize and combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory and Network Theory, fields where the consortium has internationally leading expertise. Although researchers from Computer and Network Science, Control Engineering and Applied Mathematics have proposed various approaches to tackle the above challenges, our research constitutes the first truly holistic, multidisciplinary approach that combines and extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities. Our developed theory will be extensively tested on available experimental testbed infrastructures of the participating entities. The efficiency of the overall proposed framework will be tested and evaluated under three complex use cases involving mobile autonomous agents in IoT environments: (i) distributed remote path planning of a group of mobile robots with complex specifications, (ii) rapid deployment of mobile agents for distributed computing purposes in disaster scenarios and (iii) mobility-aware resource allocation for crowded areas with pre-defined performance indicators to reach.

物联网时代丰富的传感器、执行器和通信所提供的潜力受到本地节点有限的计算能力的阻碍，使得计算在时间和空间上的分布成为必要。需要解决几个关键挑战，以便最佳地联合利用网络、计算和存储资源，同时保证时间关键型和任务关键型任务的可行性。我们的研究通过在需求快速时变时动态分配资源来应对这些挑战。我们首先提出了一个分析的数学动态建模的资源，提供的工作负载，和网络环境，其中包括在无线通信，移动的边缘计算数据中心，和网络拓扑中遇到的现象。我们还提出了一套新的估计的工作量和资源随时间变化的配置文件，不断更新的模型参数。在此框架的基础上，我们的目标是开发新的资源分配机制，明确考虑到服务的差异化和上下文感知，最重要的是，提供正式的保证定义良好的QoS/QoE指标。我们的研究远远超出了最先进的网络物理系统（CPS）的控制算法的设计，通过将资源分配机制的决策策略本身。我们提出了新一代的控制器，在网络和计算资源利用率的协同设计理念的驱动。这种模式有可能在工程的关键领域引起量子飞跃，例如，工业4.0、协作机器人、物流、多智能体系统等。为了实现这些突破，我们利用并联合收割机工具，包括自动机和图论、机器学习、现代控制理论和网络理论，这些领域是该联盟拥有国际领先专业知识的领域。虽然来自计算机和网络科学，控制工程和应用数学的研究人员提出了各种方法来应对上述挑战，但我们的研究构成了第一个真正全面的多学科方法，该方法结合并扩展了所有上述领域的最新成果，从而弥合了不同社区努力之间的差距。我们开发的理论将在参与实体的可用实验测试平台基础设施上进行广泛测试。整个拟议框架的效率将在涉及物联网环境中的移动的自主代理的三个复杂用例下进行测试和评估：（i）具有复杂规格的一组移动的机器人的分布式远程路径规划，（ii）快速部署移动的代理，用于灾难情景中的分布式计算目的，以及（iii）针对具有预先要达到的业绩指标。

项目成果

Optimal Resource Scheduling and Allocation in Distributed Computing Systems

• DOI: 10.23919/acc53348.2022.9867340
• 发表时间: 2021-10
• 期刊: 2022 American Control Conference (ACC)
• 作者: Wei Ren;E. Vlahakis;N. Athanasopoulos;R. Jungers

Distributed Resource Autoscaling in Kubernetes Edge Clusters

Kubernetes Edge 集群中的分布式资源自动扩展

• DOI: 10.23919/cnsm55787.2022.9965056
• 发表时间: 2022
• 作者: Spatharakis D

AIMD-inspired switching control of computing networks

AIMD 启发的计算网络切换控制

• DOI: 10.1109/tcns.2023.3298202
• 发表时间: 2024
• 期刊: IEEE Transactions on Control of Network Systems
• 影响因子: 4.2
• 作者: Vlahakis E

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

• DOI: 10.1016/j.comnet.2021.108177
• 发表时间: 2021-08
• 期刊: Comput. Networks
• 作者: Firdose Saeik;Marios Avgeris;Dimitrios Spatharakis;Nina Santi;Dimitrios Dechouniotis;John Violos

Polytope shaping while preserving invariance

• DOI: 10.1109/cdc51059.2022.9992851
• 发表时间: 2022-12
• 期刊: 2022 IEEE 61st Conference on Decision and Control (CDC)
• 作者: N. Athanasopoulos